A Modified Correlation for Drop Size Distribution in an Experimental Mixer-Settler using Particle Swarm Optimization Algorithm

Solvent extraction is a separation process, which is affected by many variables such as hold up, metal and extractant concentration, and input power. Therefore, process development, analysis, and optimization are complex tasks due to having strong non-linear effects, which are poorly understood. In this paper, the Particle swarm optimization (PSO) method was employed to optimize drop size distribution in an industrial process that is difficult to be optimized by conventional methods. A modified correlation was presented for D 32 by inverse analysis method. Correlation coefficients for train and test data were obtained as 0.9779 and 0.9735, respectively. The correlation has a good conformity to the experimental data.